Data warehousing and online analytical processing (OLAP) are widespread technologies employed to support business decisions and data analysis. A data warehouse is a nonvolatile repository for an enormous volume of organizational or enterprise information (e.g., 100 MB-TB). These data warehouses are populated at regular intervals with data from one or more heterogeneous data sources, for example from multiple transactional systems. This aggregation of data provides a consolidated view of an organization from which valuable information can be derived. Though the sheer volume can be overwhelming, the organization of data can help ensure timely retrieval of useful information.
Data warehouse data is often stored in accordance with a multidimensional database model. Conceptually in multidimensional database systems, data is represented as cubes with a plurality of dimensions and measures, rather than relational tables with rows and columns. A cube includes groups of data such as one or more dimensions and measures. A dimension describes an entity by which a user wants to analyze their data. A dimension is a collection of attributes that source the dimension's hierarchies and member properties. Accordingly, data can be viewed at different levels of detail. Measures represent real values, which are to be analyzed.
There are at least two primary reasons for the existence of multidimensional databases. First, the multidimensional model is optimized to deal with large amounts of data. In other words, it has a fast query response over large data volumes. Furthermore, the multidimensional model is business user friendly. This enables users execute complex queries on a data cube. OLAP is almost synonymous with multidimensional databases.
OLAP is a key element in a data warehouse system. OLAP describes a category of technologies or tools utilized to retrieve data from a data warehouse. These tools can extract and present multidimensional data from different points of view to assist and support managers and other individuals examining and analyzing data. The multidimensional data model is advantageous with respect to OLAP as it allows users to easily formulate complex queries, and filter or slice data into meaningful subsets, among other things. There are two basic types of OLAP architectures MOLAP and ROLAP. MOLAP (Multidimensional OLAP) utilizes a true multidimensional database to store data. ROLAP (Relational OLAP) utilizes a relational database to store data but is mapped so that an OLAP tool sees the data as multidimensional. HOLAP (Hybrid OLAP) is an amalgam of both MOLAP and ROLAP.
Data cube cells (and similarly members and the like) can include either fact data or functions (also referred to as calculations, expressions . . . ). Cells that include functions are called calculated cells. The value of these cells is defined by an expression in terms of one or more other cells and mathematical operations. The actual values of such cells are not known until runtime when the expressions or calculations are resolved. The formulas or expressions are defined and assigned to cells utilizing a calculation script, for example specified in a multidimensional language such as MDX (MultiDimensional eXpressions)
Even in simple OLAP applications, there can be several intersections of calculations in the multidimensional space. When these intersections occur, it is generally not clear which formula wins; such is the nature of multidimensionality. Heretofore, the process of building and debugging calculations scripts has been a difficult, time-consuming process that even the most advanced users consider to be tedious. Accordingly, there is a need in the art for novel systems and methods to facilitate development and debugging of cube calculations scripts, among other things.